Accurate post-disaster evaluation of storm surge inundation in topographically complex urban environments is often constrained by data scarcity and scale mismatches among diverse observational sources. To address the verification blind spots of discrete-point monitoring networks used to interpolate continuous spatial inundation, this study establishes a standardized rapid-response field survey methodology to capture highly reliable ground-truth data. We further propose a multi-source data fusion framework that combines spatially sparse tide gauges, temporally static post-event field surveys, and numerical hydrodynamic simulations. The framework is applied to re-evaluate the continuous urban inundation induced by Super Typhoon Ragasa (2518) in Macao. The results show that the fusion framework effectively compensates for systematic biases and refines the spatial structure of error fields, particularly when incorporating external covariates such as coastline distance and elevation. The corrected inundation assessments achieve a high-fidelity reconstruction of localized peak depths and complex street-level flood pathways, capturing micro-topographical heterogeneities that were previously overlooked. This methodology offers an effective approach for coastal flood mapping and may provide insights for disaster risk management.

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